Arbitrage Strategies Research Articles

Exploring arbitrage opportunities between China's carbon markets based on statistical arbitrage pairs trading strategy

Pairs trading is one of the mainstream statistical arbitrage strategies in current financial market practice that can seize possible arbitrage opportunities caused by mispricing between similar assets. Based on the theory of statistical arbitrage and the similar attributes of carbon emission allowances traded in different carbon markets, this paper studies the applicability of the pairs trading strategy among different pilot markets in China. The novelty and possible contribution of this paper are as follows. Firstly, this paper extends the statistical arbitrage-based pairs trading framework to the analysis of carbon market transactions in China, which broadens the understanding of the correlation between China's carbon markets. Secondly, this study explores the arbitrage opportunities among China's regional carbon markets that have not been revealed by previous studies. Thirdly, based on the pairs trading analytical model adopted in this study, this paper further investigates the operation characteristics of China's carbon markets and the interaction between markets, as the sensitivity of strategy performance to the key parameters is also discussed. The results show that arbitrage based on pairs trading is profitable. In the baseline case, the annualized arbitrage returns between the Shanghai emission allowance and Beijing emission allowance is about 9.11%, the highest among all pairs. However, there is almost no pairing relationship between the Tianjin carbon market and other markets. By discussing the time distribution of arbitrage signals generated by the pairs trading strategy, it is found that most signals are sent out during the inactive trading period in a compliance cycle. The conclusions provide valuable insights for formulating relevant carbon market regulatory policies and investors' participation in arbitrage activities in China's carbon trading markets.

Catching-up regional strategy as a tool to reduce spatial inequality

The article is devoted to the problem of spatial inequality of regions caused by centripetal or centrifugal forces because of agglomeration effects and interaction costs. It corresponds with the main idea of new economic geography. A differentiated approach to regional development strategizing is proposed in the study as a tool for reducing the level of spatial imbalance. Herein the use of technological arbitrage (catching-up) strategy is proposed for peripheral regions by strengthening interregional interaction. In the paper, two hypotheses are investigated: (1) The economic growth of territories, caused by industry diversification, positive externalities from Research and Development, the quality of human capital, which are typical of the ?center? type of regions and (2) The formation of a unified macroeconomic space is possible due to strategies of technological arbitrage (catchingup strategy) for peripheral regions. Its implementation is possible through innovation, industrial-technological, and trade-technological complementarity development of peripheral regions with the central regions. The research methods are: panel regression with fixed effects, Data Envelopment Analysis method, and Malmquist Productivity Index, paired interregional complementarity indices. The study focuses on 10 regions of Siberian Federal District in the Russia, which differ in scale, structure, and level of innovative economic development. The study results confirm the possibility of reducing the level of spatial inequality using catching-up strategies and innovation complementarity of the regions. These strategies proved only for technical efficiency leading regions with high index of complementarity. In regions with a low complementarity index and different industry profile, large-scale interregional cooperation has not been confirmed.

Relationship between deep hedging and delta hedging: leveraging a statistical arbitrage strategy

Relationship between deep hedging and delta hedging: leveraging a statistical arbitrage strategy

Predicting Chinese Commodity Futures Price: An EEMD-Hurst-LSTM Hybrid Approach

This paper proposes an EEMD-Hurst-LSTM prediction method based on the ensemble learning framework, which is applied to the prediction of typical commodities in China’s commodity futures market. This method performs ensemble empirical mode decomposition (EEMD) on commodity futures prices, and incorporates the components obtained by EEMD decomposition and the adaptive fractal Hurst index calculated by using intraday high-frequency data as new features into the LSTM model to decompose its correlation with the external market to detect changes in market conditions. The results show that the EEMD-Hurst-LSTM method has better predictive performance compared to other horizontal single models and longitudinal deep learning combined models. Meanwhile, the trading strategy designed according to this ensemble model can obtain more returns than other trading strategies and have the best risk control level. The research of this paper provides important implications for the trend following of commodity markets and the investment risk management of statistical arbitrage strategies.

Through analyzing differences between four risk hedge methods to help companies choose a suitable strategy in different financial situation

Hedging risk is an essential section of finance for all investors, especially in the times where uncertainty occurs every time. covid-19 has hit the global market heavily since 2020, promoting the importance of hedging risk significantly. This essay generally introduces 4 fundamental methods of hedging risk, including the Arbitrage strategy, alpha strategy, neutral strategy and event-driven strategy. It mainly focusses on their origins, the economics and finance theory of each format and the reality problems occurred in application. At the same time, the advantages and drawback of each methods have been discussed with full detail. General speaking , in different scenarios there might be different methods that are suitable. Alpha strategy is desirable in stable market and neutral strategy is the most secure one but requires lots of calculation. Event-driven strategy and arbitrary strategy hugely depends on the executive of managers.This essay is fairly helpful for the learner who just started to learn about hedging risks to generally study the fundamental risk hedging.

The arbitrage strategy in the crude oil futures market of shanghai international energy exchange

This research conducts an empirical study on arbitrage opportunities in the crude oil futures market of Shanghai International Energy Exchange in the period of China’s economic change, 2020–2022. We use the daily closing price data of crude oil futures sc2303 and sc2212 to test whether there is a statistical arbitrage opportunity in China’s crude oil futures market. Taking the most commonly used 12 + 6 rolling window mode in statistical arbitrage, we select several one-year periods that pass the cointegration test during the formation period and apply the optimal opening, closing, and stop loss thresholds based on the highest yield during the formation period to the trading period. Finally, we draw the following conclusions: (i) the pair trading strategy based on the cointegration model is profitable in China’s crude oil futures market; (ii) the 12 + 6 window model can be applied to the pair trading strategy based on China’s crude oil futures. Our research proves the effectiveness of pair trading strategy in China’s crude oil futures market for institutional and individual investors.

Investigating The Weekend Anomaly and Its Implications: Evidence From International Financial Markets

Purpose: As contended in prior literature, the weekend anomaly is the tendency for financial markets or security prices to be lower on Mondays than on previous Fridays. The aim of this study was to empirically investigate the weekend anomaly in seven international financial markets namely; NASDAQ Index, CAC 40 Index, DAX Index, JPX-Nikkei Index 400, SSE Index, BIST and JSE Index. Methodology: This study made use of the F- statistics test for the most recent 5 years August 22, 2017 to August 22, 2022. Findings: Contrary to the findings in the literature, there is no evidence to support the weekend anomaly. This was evident in the pvalues for the F-statistics test in all the financial markets under consideration to be statistically insignificant. Originality/Value: Although this concept may have existed, it is no longer applicable hence traders and market participants should avoid regular or pure arbitrage strategy as it may result in significant losses. As per the author’s knowledge, this study is the first to empirically investigate the weekend anomaly in seven international markets using the most recent data..

Resiliency assessment of the distribution system considering smart homes equipped with electrical energy storage, distributed generation and plug-in hybrid electric vehicles

This paper presents a novel method for resiliency assessment of the distribution system considering smart homes' arbitrage strategies in the day-ahead and real-time markets. The main contribution of this paper is that the impacts of smart homes' arbitrage strategy on the resilient operation of the distribution system are explored. The optimal commitment of smart homes in external shock conditions is another contribution of this paper. An arbitrage index is proposed to explore the impacts of this process on the system costs and resiliency of the system. A two-level optimization process is proposed for day-ahead and real-time markets. At the first stage of the first level, the optimal bidding strategies of smart homes are estimated for the day-ahead market. Then, the database is updated and the optimal bidding strategies of smart homes for real-time horizon are assessed in the second stage of the first level problem. At the first stage of the second level problem, the optimal day-ahead scheduling of the distribution system is performed considering the arbitrage and resiliency indices. At the second stage of the second level, the distribution system optimal scheduling is carried out for the real-time horizon. Finally, at the third stage of the second level, if an external shock is detected, the optimization process determines the optimal dispatch of system resources. The proposed method is assessed for the 33-bus and 123-bus IEEE test systems. The proposed framework reduced the expected values of aggregated costs of 33-bus and 123-bus systems by about 62.14 % and 32.06 % for the real-time horizon concerning the cases in which the smart homes performed arbitrage strategies. Furthermore, the average values of the locational marginal price of 33-bus and 123-bus systems were reduced by about 59.38 % and 63.98 % concerning the case that the proposed method was not implemented.

Going, Going, Gone: Optimising the bidding strategy for an energy storage aggregator and its value in supporting community energy storage

Energy storage aggregation can bring many advantages to electrical power systems; these include improving flexibility, lowering system costs and reducing the need for carbon-intensive peaking plants. In this work linear and quadratic optimisation models are applied to examine the value derived by an aggregator, using energy storage for arbitrage in Great Britain’s day-ahead and balancing energy markets. The optimum arbitrage strategy is determined when the aggregator is considered a price-taker and a price-maker with access of up to 500 MW battery storage. In the day-ahead market the choice of price-taker or price-maker strategy made negligible difference, however, in the balancing market the price-maker profits were 10.9% higher for 500 MW storage when prices were perfectly forecasted. For imperfect forecasting, the choice of strategy made a negligible difference. A sensitivity analysis challenged the modelling assumptions. System parameters that had the greatest impact on results were the battery efficiency (more efficient, higher profits) and its duration (shorter duration, higher profits). The model was then applied to investigate the potential value that can be obtained by an aggregator with partial access to a community-owned energy storage. Results showed that household electricity bills could be improved by this arrangement. In particular, households that use their batteries less during the winter months, could improve their profits by up to 13.2% if aggregator access is allowed.

Determining the Power and Capacity of Electricity Storage in Cooperation with the Microgrid for the Implementation of the Price Arbitration Strategy of Industrial Enterprises Installation

The growing worldwide costs of energy produced as a result of conventional fuel combustion, the limited capacity of the distribution grid, and the growing number of unstable installations based on renewable energy sources increase the need to implement systems of stabilization and regulate loads for end users. The battery energy storage system (BESS) that operates in the internal microgrid of an enterprise enables the management of the accumulated energy in any time zone of the day. Using a price arbitrage strategy with an electricity storage facility, we can reduce the cost of high electricity prices during peak demand periods. This study aims to determine the most effective method of setting up the capacity and electrical power of an energy storage system operating in a microgrid, in an enterprise to implement a price arbitration strategy. Such a method should include consideration of the characteristics of the demand profile of consumer systems, the charges related to electricity, and electricity storage costs. The proposed deterministic method is based on the use of a defined parameter, “marginal income elasticity”. In this study, the size of energy storage refers to the power and electric capacity of BESS that are used for the implementation of the price arbitrage strategy.

Model-Free Bounds for Multi-Asset Options Using Option-Implied Information and Their Exact Computation

We consider derivatives written on multiple underlyings in a one-period financial market, and we are interested in the computation of model-free upper and lower bounds for their arbitrage-free prices. We work in a completely realistic setting, in that we only assume the knowledge of traded prices for other single- and multi-asset derivatives and even allow for the presence of bid–ask spread in these prices. We provide a fundamental theorem of asset pricing for this market model, as well as a superhedging duality result, that allows to transform the abstract maximization problem over probability measures into a more tractable minimization problem over vectors, subject to certain constraints. Then, we recast this problem into a linear semi-infinite optimization problem and provide two algorithms for its solution. These algorithms provide upper and lower bounds for the prices that are ε-optimal, as well as a characterization of the optimal pricing measures. These algorithms are efficient and allow the computation of bounds in high-dimensional scenarios (e.g., when d = 60). Moreover, these algorithms can be used to detect arbitrage opportunities and identify the corresponding arbitrage strategies. Numerical experiments using both synthetic and real market data showcase the efficiency of these algorithms, and they also allow understanding of the reduction of model risk by including additional information in the form of known derivative prices. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported by the Nanyang Technological University [NAP Grant] and the Hellenic Foundation for Research and Innovation [Grant HFRI-FM17-2152]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4456 .

Statistical Arbitrage for Multiple Co-integrated Stocks.

In this article, we analyse optimal statistical arbitrage strategies from stochastic control and optimisation problems for multiple co-integrated stocks with eigenportfolios being factors. Optimal portfolio weights are found by solving a Hamilton–Jacobi–Bellman (HJB) partial differential equation, which we solve for both an unconstrained portfolio and a portfolio constrained to be market neutral. Our analyses demonstrate sufficient conditions on the model parameters to ensure long-term stability of the HJB solutions and stable growth rates for the optimal portfolios. To gauge how these optimal portfolios behave in practice, we perform backtests on historical stock prices of the S&P 500 constituents from year 2000 through year 2021. These backtests suggest three key conclusions: that the proposed co-integrated model with eigenportfolios being factors can generate a large number of co-integrated stocks over a long time horizon, that the optimal portfolios are sensitive to parameter estimation, and that the statistical arbitrage strategies are more profitable in periods when overall market volatilities are high.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00245-022-09838-3.

BESS frequency regulation strategy on the constraints of planned energy arbitrage using chance-constrained programming

Battery energy storage systems (BESS) are regarded as a multi-functional power system participator, participating in the energy arbitrage strategy (EAS), the frequency regulation strategy (FRS) and so on. However, the existing BESS mainly make profit from the EAS instead of the FRS in many countries such as China. Because there is no appropriate control strategy for BESS, when the developing ancillary services market cannot price the contribution of BESS in FRS. Meanwhile, BESS do have redundant power and capacity, if only the EAS is applied. Therefore, it is significant that BESS are involved in the FRS, that the EAS has the priority in BESS utilization. In this paper, a frequency regulation strategy for the user-side BESS is proposed, on the constraints of the planned energy arbitrage. Specifically, the chance-constrained programming is applied to solve the randomness of frequency regulation requirement, ensuring the BESS profit from the EAS. Also, the exiting BESS are fully used with extra profit from FRS. At last, the promising results prove the advantages of the proposed FRS.

Shorting costs and profitability of long–short strategies

AbstractWe examine how the profitability of long–short arbitrage strategies based on anomalies is affected after adjustment for two shorting costs: implicit cost due to unavailability of stocks in the short‐leg to sell short and loan fees actually paid to stock lenders. The combined shorting cost amounts to almost 40 percent of long–short gross returns over the sample period from January 2006 to December 2017. After adjustment for these shorting costs, long–short arbitrage profits are thus reduced by almost 40 percent. Even after adjustment for risk, the proportion of shorting costs is also substantial. If other trade‐related transaction costs are considered, long–short arbitrage profits would be reduced further. Our results provide explicit evidence that casts doubt on the profitability of long‐short arbitrage strategies based on anomalies.

Is there an analyst (un)coverage premium?

We investigate whether the variation in the level of information availability relates to a performance differential across share prices. Using the number of analyst reports on the stock as proxy, we study twenty countries’ stock markets and find that there exists a positive analyst (un)coverage return in fifteen countries with twelve of them statistically significant. This suggests a premium on stocks which are not followed by analysts. A zero-investment arbitrage strategy that longs the no-coverage stocks and shorts the high-coverage stocks in equal-weighted portfolios generates an average of 0.34% in monthly alpha across the twenty countries studied.

Purchasing Power Parity Between Vietnam and United States

This study uses the annual data of consumer price index, exchange rate and inflation rates spanned from 1986 to 2014 in order to observe whether the PPP hypothesis holds between Vietnam and United States. First, the findings come out based on the graph aproach which is used in order to examine both short-run and long-run PPP. Second, the Engle-Granger approach is applied in order to test for the long – run PPP again. In the short-run, the result from the graph approach indicates that PPP holds during hyperinflation years. After hyperinflation period, the exchange rate and relative price tend to be close together in several years. This finding in the short-run seems to reveal a good guide in the long-run PPP. Both graph and the Engle-Granger approach show the same result in the long –run. Upon the stationary testing, the finding of the Engle-Granger approach demonstrates that residuals from the estimation are stationary, therefore, there is an existence of a long-run relationship between nominal and real exchange rate. In other way, PPP seems to hold between Vietnam and United States. The result leads to the implication that multinational companies should set the same prices for products in Vietnam and United States market based on PPP. Otherwise, investors can gain profit through arbitrage strategies.

Optimal scheduling of an active distribution system considering distributed energy resources, demand response aggregators and electrical energy storage

This paper presents a two-level optimization model for the optimal scheduling of an active distribution system in day-ahead and real-time market horizons. The distribution system operator transacts energy and ancillary services with the electricity market, plug-in hybrid electric vehicle parking lot aggregators, and demand response aggregators. Further, the active distribution system can utilize a switching procedure for its zonal tie-line switches to mitigate the effects of contingencies. The main contribution of this paper is that the proposed framework simultaneously models the arbitrage strategy of the active distribution system, electric vehicle parking lot aggregators, and demand response aggregators in the day-ahead and real-time markets. This paper's solution methodology is another contribution that utilizes robust and lexicographic ordering optimization methods. At the first stage of the first level, the optimal bidding strategies of plug-in hybrid electric vehicle parking lot aggregators and demand response aggregators are explored. Then, at the second stage of the first level, the day-ahead optimization process finds the optimal scheduling of distributed energy resources and switching of electrical switches. Finally, at the second level, the real-time optimization problem optimizes the scheduling of system resources. Different case studies were carried out to assess the effectiveness of the algorithm. The proposed algorithm increases the system's day-ahead and real-time revenues by about 52.09% and 47.04% concerning the case without the proposed method, respectively.

Assessing the influence of solar forecast accuracy on the revenue optimization of photovoltaic + battery power plants in day-ahead energy markets

Energy storage has become a subject of great interest in the last years due to the increasing penetration of non-dispatchable renewable energy power plants, especially solar photovoltaics (PV) and wind. Energy storage systems can add value to the grid in many ways: energy schedule optimization, ancillary services, renewable energy integration at high penetration levels, and so on. However, from the perspective of the asset's owner, it is important that the system regulator provides a regulatory framework and energy prices that stimulate the private plant operator to dispatch its energy storage system to assist the grid and get rewarded by these operations. An optimization problem is presented from the perspective of an asset owner with a PV plant and battery energy storage system. From this perspective, the agent must know the day-ahead spot-market energy prices and predict the day-ahead PV generation to decide on how to dispatch the battery system. We have evaluated 11 forecast accuracies to assess how the forecast accuracy can impact on the agent's total revenue.The findings showed that, if the agent's objective is to use the battery system only to guarantee the contracted energy, the battery system does not add as much value as it could; and higher forecast uncertainties lead to higher revenues, because the battery will then operate closer to the tariff arbitrage strategy, which is the optimal strategy for battery dispatch, independent of PV generation, relying only on the spot-market energy prices.

International High-Frequency Arbitrage for Cross-Listed Stocks

We explore latency arbitrage activities with a new arbitrage strategy that we test with high-frequency data during the first six months of 2019. We study the profitability of mean-reverting arbitrage activities of 74 cross-listed stocks involving three exchanges in Canada and the United States. Our arbitrage strategy is a hybrid between triangular arbitrage and pairs trading. We synchronize the high-frequency data feeds from the three exchange venues considering explicitly the latency that comes from the transportation of information between the exchanges and its treatment time. Other trading costs and arbitrage risks are also considered. The annual net profit of an HFT firm that uses limit orders is around CAD $8 million (USD $6 million), a result that we consider reasonable when compared with the previous literature. International latency arbitrage with market orders is never profitable.

Does Indian Commodity Futures Markets Exhibit Price Discovery? An Empirical Analysis

Price discovery function analyses the dynamics of futures and spot price behavior in an asset’s intertemporal dimensions. The present study examines the price discovery function of the bullion, metal, and energy commodity futures and spot prices through the Granger causality and Johansen–Juselius cointegration tests. The Granger causality test results show bidirectional causality between the spot and futures returns for gold, silver, aluminum, lead, nickel, and zinc. The Johansen cointegration test shows that spot and futures prices are in the long‐run equilibrium path for silver, aluminum, lead, nickel, zinc, crude oil, and natural gas. The vector error correction model results suggest that both the spot and futures markets are equally efficient in price discovery for the nickel. The spot market leads the futures market in price discovery for copper and zinc. However, the futures market leads the spot market in price discovery for silver, aluminum, and lead. The findings of the study suggest the market participants for implementing hedging and arbitrage strategies. It also helps the market regulators to examine the stability of these rapidly growing commodity futures markets in India.